AI Beam Set Control for 5G Link Management
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Solution Overview
Problem
Existing wireless communication systems lack efficient link control methods that leverage artificial intelligence and machine learning to optimize beam management in high-frequency bands, particularly in 5G and beyond, leading to suboptimal performance and compatibility issues.
Innovation Solution
A method and apparatus that utilize AI/ML models for generating and reporting optimized transmission beam sets based on measurement information, enabling intelligent link control through AI/ML-based radio link management, including configuration and activation/deactivation of beam sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional beam management methods are used in high-frequency bands, then system compatibility is maintained, but beam management efficiency and communication performance deteriorate
Solution Approach 1:
The patent changes the parameter of beam management from traditional fixed methods to AI/ML-based dynamic methods. The base station uses AI models to process measurement information and determine optimal transmission beam sets, transforming the beam management approach while maintaining compatibility through standardized interfaces and procedures defined in 3GPP protocols.
Solution Approach 2:
The patent replaces traditional mechanical beam management systems with AI/ML-based intelligent systems. Instead of relying on predetermined beam selection algorithms, the system uses machine learning models to intelligently process measurement information and automatically determine optimal beam configurations, achieving superior performance while maintaining system compatibility through standardized protocols.
2Reliability
If AI/ML-based beam management is implemented, then communication performance and beam optimization improve, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary AI model that acts as a bridge between measurement information and beam management decisions. The AI model processes complex measurement data and outputs optimized beam configurations, simplifying the overall system architecture while improving performance. The model serves as an intelligent intermediary that handles the complexity internally while presenting simplified interfaces to the network.
Solution Approach 2:
The patent segments the beam management function into distinct components: measurement information collection, AI model processing, and beam determination. This segmentation allows each component to be optimized independently and facilitates integration with existing network architectures, reducing overall system complexity while enabling advanced AI/ML-based optimization.
3Measurement precision
If measurement information is collected for all beams, then beam selection accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent applies partial action by having the AI model process measurement information selectively rather than uniformly for all beams. The model identifies which measurement data are most critical for accurate beam selection and focuses computational resources on processing those specific measurements, achieving high accuracy while reducing overall processing time and computational load.
Data Source
AI summary
A method of a user equipment (UE) may comprise: receiving, from a base station, first configuration information including a first transmission beam set; in response to a preconfigured condition being satisfied, receiving, from the base station, a reference signal (RS) through beams corresponding to the first transmission beam set; generating measurement information for the RS received through each of the beams corresponding to the first transmission beam set; generating a second transmission beam set based on the measurement information using an artificial intelligence (AI) model; and reporting information on the second transmission beam set to the base station, wherein the measurement information is an input of the AI model, and the second transmission beam set is generated through inference of the AI model.